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Статті в журналах з теми "Functional connectomes"
Seguin, Caio, Ye Tian, and Andrew Zalesky. "Network communication models improve the behavioral and functional predictive utility of the human structural connectome." Network Neuroscience 4, no. 4 (January 2020): 980–1006. http://dx.doi.org/10.1162/netn_a_00161.
Повний текст джерелаKesler, Shelli R., Paul Acton, Vikram Rao, and William J. Ray. "Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease." Network Neuroscience 2, no. 2 (June 2018): 241–58. http://dx.doi.org/10.1162/netn_a_00048.
Повний текст джерелаChen, Vincent Chin-Hung, Tung-Yeh Lin, Dah-Cherng Yeh, Jyh-Wen Chai, and Jun-Cheng Weng. "Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy." Brain Sciences 10, no. 11 (November 12, 2020): 851. http://dx.doi.org/10.3390/brainsci10110851.
Повний текст джерелаRajapandian, Meenusree, Enrico Amico, Kausar Abbas, Mario Ventresca, and Joaquín Goñi. "Uncovering differential identifiability in network properties of human brain functional connectomes." Network Neuroscience 4, no. 3 (January 2020): 698–713. http://dx.doi.org/10.1162/netn_a_00140.
Повний текст джерелаBaker, Justin T., Daniel G. Dillon, Lauren M. Patrick, Joshua L. Roffman, Roscoe O. Brady, Diego A. Pizzagalli, Dost Öngür, and Avram J. Holmes. "Functional connectomics of affective and psychotic pathology." Proceedings of the National Academy of Sciences 116, no. 18 (April 15, 2019): 9050–59. http://dx.doi.org/10.1073/pnas.1820780116.
Повний текст джерелаGarzón, Benjamín, Martin Lövdén, Lieke de Boer, Jan Axelsson, Katrine Riklund, Lars Bäckman, Lars Nyberg, and Marc Guitart-Masip. "Role of dopamine and gray matter density in aging effects and individual differences of functional connectomes." Brain Structure and Function 226, no. 3 (January 9, 2021): 743–58. http://dx.doi.org/10.1007/s00429-020-02205-4.
Повний текст джерелаOsmanlioglu, Yusuf, Drew Parker, Steven Brem, Ali Shokoufandeh, and Ragini Verma. "NIMG-69. PERSONALIZED CONNECTOMIC SIGNATURES: BRIDGING THE GAP BETWEEN NEURO-ONCOLOGY AND CONNECTOMICS." Neuro-Oncology 22, Supplement_2 (November 2020): ii163. http://dx.doi.org/10.1093/neuonc/noaa215.682.
Повний текст джерелаMa, Qing, Yanqing Tang, Fei Wang, Xuhong Liao, Xiaowei Jiang, Shengnan Wei, Andrea Mechelli, Yong He, and Mingrui Xia. "Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study." Schizophrenia Bulletin 46, no. 3 (November 22, 2019): 699–712. http://dx.doi.org/10.1093/schbul/sbz111.
Повний текст джерелаHyon, Ryan, Yoosik Youm, Junsol Kim, Jeanyung Chey, Seyul Kwak, and Carolyn Parkinson. "Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village." Proceedings of the National Academy of Sciences 117, no. 52 (December 14, 2020): 33149–60. http://dx.doi.org/10.1073/pnas.2013606117.
Повний текст джерелаGatica, Marilyn, Fernando E. Rosas, Pedro A. M. Mediano, Ibai Diez, Stephan P. Swinnen, Patricio Orio, Rodrigo Cofré, and Jesus M. Cortes. "High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model." PLOS Computational Biology 18, no. 9 (September 2, 2022): e1010431. http://dx.doi.org/10.1371/journal.pcbi.1010431.
Повний текст джерелаДисертації з теми "Functional connectomes"
Bollmann, Yannick. "Emergence of functional and structural cortical connectomes through the developmental prism." Thesis, Aix-Marseille, 2019. http://theses.univ-amu.fr.lama.univ-amu.fr/191113_BOLLMANN_844bezee521trbla166eo565zm_TH.pdf.
Повний текст джерелаCortical neurons are generated throughout an extended embryonic period. Recent studies indicate that the cells originating from the earliest stages of neurogenesis are critically involved in coordinating neuronal activity, instructing network maturation throughout large cortical areas. The first part of my work was building and mining brain cell atlases and connectomes. I first characterized the brain-wide structural connectome of early-born glutamatergic and GABAergic neurons, fluorescently labeled according to their date of birth (genetic fate-mapping approach). Using light-sheet microscopy on cleared brains, I quantify the distribution of both populations in the whole brain to create an Atlas.The second part of my work was the characterization of GABAergic neurons functional connectome and the characterization of hub cells in the developing barrel cortex in vivo. By using transgenic mice lines expressing the calcium indicator GCaMP6s, we follow the maturation and the functional dynamics of the network during the two first postnatal weeks using two-photon imaging. The characteristically heavy-tailed distribution of functional connections between neurons that we observed, strongly suggest the presence of hub neurons. Using two-photon calcium imaging and holographic-optogenetic stimulation we entangle the necessary and sufficient conditions of how GABAergic neurons contribute to and synchronize network activity as acting as hub neuron in the barrel cortex
Afyouni, Soroosh. "Application of graph theoretical models to the functional connectome of human brain." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/88528/.
Повний текст джерелаMahama, Edward Kofi. "Connectome eigenmodes underlies functional connectivity patterns in conscious awake and anesthetic mice." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/880.
Повний текст джерелаKundu, Prantik. "Physical analysis of BOLD fMRI signals for functional brain mapping and connectomics." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648842.
Повний текст джерелаMelozzi, Francesca. "Simulated switching of the resting state functional connectivity in mouse brain using a real mesoscale connectome." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8319/.
Повний текст джерелаMelozzi, Francesca. "The role of structural brain features on resting-state functional organization : a large-scale computational study in mice." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0771.
Повний текст джерелаThe connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system and then studying how the functional architecture rises from the underlying structural skeleton. In this thesis, taking advantage of mice studies, we investigated the informative content of different structural features in explaining the functional ones.First, we extended the open-source software TVB (Leon et al., 2013), originally designed for humans, to accommodate the connectome-based model approach in mice (Melozzi et al., 2017).Using diffusionMRI (dMRI) data from 19 mice, we virtualised their brains to generate in silico fMRI that we compared to functional MRI data recorded in the same mice during passive wakefulness. We show that the predictions of the connectome-based model strictly depend on the structure of the underlying network (Melozzi et al., under review). We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains. Comparing the predictive power of the tracer-based and the dMRI-based connectome we identify how the limitations of the dMRI method restrict our comprehension of the structural-functional relation. Together, these results strongly support the existence of a causal link between the structural and the functional connectomes.Finally, we infer the connectome form resting state dynamics by inferring the structural connectome using the Bayesian inference (Melozzi et al., in prep).Our results pave the way to future studies focusing on the causal link between structure and function at the individual brain level
Hart, Michael Gavin. "Network approaches to understanding the functional effects of focal brain lesions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274018.
Повний текст джерелаVáša, František. "Characterising disease-related and developmental changes in correlation-derived structural and functional brain networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277816.
Повний текст джерелаColclough, Giles. "Methods for modelling human functional brain networks with MEG and fMRI." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:ef1dc66e-f142-4cdc-8177-5d040c94b964.
Повний текст джерелаSuprano, Ilaria. "Étude de la connectivité cérébrale par IRM fonctionnelle et de diffusion dans l’intelligence." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1282.
Повний текст джерелаThe idea that intelligence is embedded not only in specific brain regions, but also in efficient brain networks has grown up. Indeed, human brain organization is believed to rely on complex and dynamic networks in which the communication between cerebral regions guarantees an efficient transfer of information. These recent concepts have led us to explore the neural bases of intelligence using both advanced MRI techniques in combination with graph analysis. On one hand, advanced MRI techniques, such as resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) allow the exploration of respectively the functional and the structural brain connectivity while on the other hand, graph theory models allow the characterization of brain networks properties at different scales, thanks to global and local metrics. The aim of this thesis is to characterize the topology of functional and structural brain networks in children and in adults with an intelligence quotient higher (HIQ) than standard levels (SIQ). First, we focused our attention on a children population with different cognitive characteristics. Two HIQ profiles, namely homogeneous (Hom-HIQ) and heterogeneous HIQ (Het-HIQ), have been defined based on clinical observations and Intelligence Quotient (IQ) sub-tests. Using resting-state fMRI techniques, we examined the functional network topology changes, estimating the "hub disruption index", in these two HIQ profiles. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to SIQ children, for the whole brain graph, for each hemispheric graph, and for the homotopic connectivity. These brain networks changes resulted to be more pronounced in Het-HIQ subgroup. Finally, we found significant correlations between the graph networks’ changes and the full-scale IQ, as well as some intelligence subscales. These results demonstrated for the first time, that different HIQ profiles are related to a different neural substrate organization. Then, the structural brain network connectivity, measured by dMRI in all HIQ children, were significantly different than in SIQ children. Also, we found strong correlations between the children brain networks density and their intelligence scores. Furthermore, several correlations were found between integration graph metrics suggesting that intelligence performances are probably related to a homogeneous network organization. These findings demonstrated that intelligence neural substrate is based on a strong white matter microarchitecture of the major fiber-bundles and a well-balanced network organization between local and global scales. This children population was finally studied using a memory-word task of fMRI. Significant changes were observed between both HIQ and SIQ groups. This study confirms our hypothesis that both HIQ profiles are characterized by a different brain activity, with stronger evidences in Het-HIQ children. Finally, we investigated both functional and structural connectivity in a population of adults HIQ. We found several correlations between graph metrics and intelligence sub-scores. As well as for the children population, high cognitive abilities of adults seem to be related brain structural and functional networks organization with a decreased modularity. In conclusion, the sensitivity of graph metrics based on advanced MRI techniques, such as rs-fMRI and dMRI, was demonstrated to be very helpful to provide a better characterization of children and adult HIQ, and further, to distinguish different intelligence profiles in children
Книги з теми "Functional connectomes"
Shamlan, Muhsin Ahmad Bin. The function of Arabic connectors in clause relations. Salford: University of Salford, 1987.
Знайти повний текст джерелаZuo, Xi-Nian, Bharat B. Biswal, and Russell A. Poldrack, eds. Reliability and Reproducibility in Functional Connectomics. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-821-9.
Повний текст джерелаvon Philipsborn, Anne C. Neurobiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0003.
Повний текст джерелаЧастини книг з теми "Functional connectomes"
Carlson, Kristen W., Jay L. Shils, Longzhi Mei, and Jeffrey E. Arle. "Functional Requirements of Small- and Large-Scale Neural Circuitry Connectome Models." In Brain and Human Body Modeling 2020, 249–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_14.
Повний текст джерелаChuhma, Nao. "Optogenetic Analysis of Striatal Connections to Determine Functional Connectomes." In Optogenetics, 265–77. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55516-2_18.
Повний текст джерелаDadashkarimi, Javid, Amin Karbasi, and Dustin Scheinost. "Data-Driven Mapping Between Functional Connectomes Using Optimal Transport." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 293–302. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_28.
Повний текст джерелаKhosla, Meenakshi, Keith Jamison, Amy Kuceyeski, and Mert R. Sabuncu. "3D Convolutional Neural Networks for Classification of Functional Connectomes." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 137–45. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_16.
Повний текст джерелаChung, Ai Wern, and Markus D. Schirmer. "Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism." In Connectomics in NeuroImaging, 126–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_13.
Повний текст джерелаSchirmer, Markus D., and Ai Wern Chung. "Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism." In Connectomics in NeuroImaging, 54–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_6.
Повний текст джерелаLisowska, Anna, and Islem Rekik. "Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes." In PRedictive Intelligence in MEdicine, 103–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00320-3_13.
Повний текст джерелаKozlov, Stanislav, Alexey Poyda, Vyacheslav Orlov, Maksim Sharaev, and Vadim Ushakov. "Selection of Functionally Homogeneous Human Brain Regions for Functional Connectomes Building Based on fMRI Data." In Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics, 709–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71637-0_82.
Повний текст джерелаDadashkarimi, Javid, Amin Karbasi, and Dustin Scheinost. "Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport." In Lecture Notes in Computer Science, 386–95. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16431-6_37.
Повний текст джерелаSvaldi, Diana O., Joaquín Goñi, Apoorva Bharthur Sanjay, Enrico Amico, Shannon L. Risacher, John D. West, Mario Dzemidzic, Andrew Saykin, and Liana Apostolova. "Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes." In Lecture Notes in Computer Science, 74–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00689-1_8.
Повний текст джерелаТези доповідей конференцій з теми "Functional connectomes"
Li, Xiang, Dajiang Zhu, Xi Jiang, Changfeng Jin, Lei Guo, Lingjiang Li, and Tianming Liu. "Discovering common functional connectomics signatures." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556551.
Повний текст джерелаChen, Hanbo, Kaiming Li, Dajiang Zhu, and Tianming Liu. "Identifying consistent brain networks via maximizing predictability of functional connectome from structural connectome." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556640.
Повний текст джерелаSachdeva, Pratik S., Sharmodeep Bhattacharyya, and Kristofer E. Bouchard. "Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856316.
Повний текст джерелаChen, Bo, and Xiang Li. "Temporal functional connectomics in schizophrenia and healthy controls." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123054.
Повний текст джерелаWang, Dong, Zhenyue Shi, Xu Zhang, DongYa Jing, Zhongjing Hu, and Hongxu Su. "Research on shear performance of new prefabricated sandwich thermal insulation exterior wall connectors." In 2022 International Conference on Optoelectronic Information and Functional Materials (OIFM 2022), edited by Chao Zuo. SPIE, 2022. http://dx.doi.org/10.1117/12.2638716.
Повний текст джерелаXing, Xiaodan, Lili Jin, Feng Shi, and Ziwen Peng. "Diagnosis of OCD using functional connectome and Riemann kernel PCA." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512316.
Повний текст джерелаIakovidou, Nantia D., Stavros I. Dimitriadis, Nikos A. Laskaris, and Kostas Tsichlas. "Querying functional brain connectomics to discover consistent subgraph patterns." In 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2013. http://dx.doi.org/10.1109/bibe.2013.6701655.
Повний текст джерелаWang, Peng, Dajiang Zhu, Xiang Li, Hanbo Chen, Xi Jiang, Li Sun, Qingjiu Cao, Li An, Tianming Liu, and Yufeng Wang. "Identifying functional connectomics abnormality in attention deficit hyperactivity disorder." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556532.
Повний текст джерелаDi Marco, Antinisca, Paola Inverardi, and Romina Spalazzese. "Synthesizing self-adaptive connectors meeting functional and performance concerns." In 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 2013. http://dx.doi.org/10.1109/seams.2013.6595500.
Повний текст джерелаTaya, Fumihiko, Yu Sun, Nitish Thakor, and Anastasios Bezerianos. "Information transfer efficiency during rest and task a functional connectome approach." In 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2014. http://dx.doi.org/10.1109/biocas.2014.6981655.
Повний текст джерела